Modelling Simple Toxicity Endpoints: Alerts, (Q)Sars And Beyond

The correlation of chemical structure with physicochemical and biological data to assess a desired or undesired biological outcome now utilises both qualitative and quantitative structure-activity relationships ((Q)SARs) and advanced computational methods. The adoption of in silico methodologies for predicting toxicity, as decision support tools, is now a common practice in both developmental and regulatory contexts for certain toxicity endpoints. The relative success of these tools has unveiled further challenges relating to interpreting and applying the results of models. These include the concept of what makes a negative prediction and exploring the use of test data to make quantitative predictions. Due to several factors, including the lack of understanding of mechanistic pathways in biological systems, modelling complex endpoints such as organ toxicity brings new challenges. The use of the adverse outcome pathway (AOP) framework as a construct to arrange models and data, to tackle such challenges, is reviewed.